Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging

Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains. Graphic abstract: Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm.

approach is conceptually different from the more traditional region of interest (ROI) approach, in which structural changes in specific brain regions or connectomes are examined (Helmstaedter et al., 2013;Takemura et al., 2013;Saleeba et al., 2019). Nonetheless, dMRI studies are highly complementary with traditional neuroscience approaches, as structural changes identified by dMRI can be further examined using microscopy and genomic/proteomic approaches, and their contribution to complex behavior can be rigorously tested using chemogenetic and optogenetic tools (Kaffman et  With the development of high-resolution dMRI acquisition and tractography methods, dMRI tractography can now quickly survey macroscopic structural connectivity in the entire brain without sectioning, which is time consuming and prone to distortions and tissue damage (Moldrich et al., 2010;Wu et al., 2013;Calabrese et al., 2015;Xiong et al., 2018). It also permits simultaneous examination of multiple white matter connections in the same specimen, further reducing the time and cost. With the latest tools for brain connectivity analysis, tractography results can be used to examine changes at both individual pathways and entire connectome levels (Edwards et al., 2020). dMRI also has several drawbacks, including lower resolution in gray matter regions compared to T1/T2-weighted MRI (Dorr et al., 2008;White et al., 2020) and limited spatial resolution and specificity compared to light microscopy findings with chemical or viral tracers (Wu and Zhang, 2016;Edwards et al., 2020). The need to rigorously correct for multiple comparisons when conducting whole-brain voxel analysis further hinders the detection of subtle changes and is particularly challenging when looking for interaction between two variables, such as early life stress (ELS) and sex (White et al., 2020). High resolution dMRI usually requires perfusing the animal, which prevents longitudinal rescanning of the same animals. Although techniques for in vivo high resolution dMRI of rodent brains have emerged (Wu et al., 2013;Wu et al., 2014), the exposure to anesthesia during MRI (2-3 h per session) may introduce additional confounding factors. Therefore, portraying a standardized procedure for reliable and reproducible estimation of microstructural changes in the mouse brain is crucial.
The protocol described here covers image acquisition, whole brain voxel analyses for volumetric and FA changes, tractography, and analysis. Compared to similar methods described before (Calabrese et al., 2015;Edwards et al., 2020), this protocol is based on the structural labels in the Allen Mouse Brain Atlas, which makes it relatively straightforward to compare tractography results with viral tracer results in the Allen Mouse Brain Connectivity Atlas (Oh et al., 2014;White et al., 2020). Unbiased wholebrain voxel analyses were used to identify brain regions that show changes in volume and dMRI parameters (e.g., FA) induced by ELS, and to compare them with those reported in humans exposed to early adversity. Fourteen brain regions that showed structural changes were used as nodes to generate a 14 × 14 matrix in each hemisphere. The network properties of this grid were then characterized using graph theory and compared with findings in humans exposed to early adversity (White et al., 2020). Our 4 www.bio-protocol.org/e4221  protocol relies on precise image registration to transfer structural labels from the atlas to subject images and will not work when there are large tissue deformations, such as those caused by brain tumors or severe necrosis. The protocol also has a node-to-node analysis step for small connections (e.g., in the amygdala network) that may be obscured in a whole brain analysis. Altogether, the protocol is useful for characterizing whole brain structural connectivity in mouse models of diseases.  6. Store the sample at 4°C for one week for the gadodiamide to diffuse into the tissue. 7. Trim the skin and muscle tissues but keep the skull and eyeballs intact ( Figure 1A). Remove the mandible bone and the tongue. 8. Place one brain in the barrel of a 5 ml syringe, with the nose facing the hub of the syringe, then place 2-3 small pieces of bent zip-tie at the bottom and back of the brain to properly fix the specimen within the syringe barrel ( Figure 1A). 9. Replacing the cap of the syringe with a loosely tied vacutainer, fill the syringe with perfluoropolyether (Fomblin ® ), insert the plunger, flip the syringe so that the hub points upward, and remove the cap. B. MR data acquisition 1. Place the syringe horizontally in the animal holder for the cryogenic probe and adjust the sample position so that the dorsal part of the brain is as close to the cryogenic coil as possible to maximize sensitivity. Use tape to fix the syringe to the animal holder ( Figure 1D).
2. Insert the animal holder into the magnet under the cryogenic probe ( Figure 1E).
3. Acquire a pilot scan using the Bruker Localizer protocol. For any MRI studies, Localizer is the very first scan that acquires reference images of the subject in three orthogonal planes. The images of the resulting scan appear in the 'geometry editor,' where the first three viewports show the reference brain slices in axial, sagittal, and coronal orientations. Therefore, the Localizer provides a quick view of the specimen in the magnet ( Figure 1F). Check whether the sample is in the most sensitive region of the cryogenic probe with no apparent tilt toward the left or right sides. Adjust the position of the subject and re-run the Localizer protocol prior to proceeding to the next step.

Data analysis
A. Data pre-processing For each dataset, perform the following steps accordingly: 1. Motion correction: Using DTIStudio (Jiang et al., 2006), align all DWIs to the average of b0s to remove small sample displacements due to vibrations and B0 field drift during the long scan ( Figure 2A).
2. Skull-stripping: Use AMIRA segmentation editor to remove non-brain tissues and define the subject specific whole brain mask ( Figure 2B).  Figure 3A). Next, transfer the structural labels (i.e., brain regions or nodes) to the subject's-native space using the inverse mapping from LDDMM ( Figure 3B, also see Note 1). If DiffeoMap is not available, ANTs (http://stnava.github.io/ANTs/) can be used instead ( Figure 2D). 9 www.bio-protocol.org/e4221   A. Co-registration of the MR data (subject) into group averaged mouse brain atlas template using multi-channel LDDMM. B. Transformation of structural labels from MRI-based atlas to subject's native space.

Assessment of brain microstructural changes
At first, compute the Jacobian determinant value for each voxel from the mapping between atlas and subject images generated by LDDMM, and conduct whole-brain voxel-based morphometric analysis in Matlab to identify local volumetric changes affected by rearing, sex, and their interaction (2 × 2 ANOVA, FDR corrected, α = 0.1, P < 0.0105, cluster size > 25 voxels). See the Matlab codes (source data 1) used to conduct 2 × 2 ANOVA (White et al., 2020). Then, similarly perform 2 × 2 ANOVA (FDR corrected, α = 0.1, P < 0.007, cluster size > 25 voxels) to examine the voxel-wise changes in FA (White et al., 2020). These analyses will provide unbiased overviews of morphometric changes due to rearing, sex, and rearing by sex interaction.

Selection of brain regions (nodes) for structural connectivity assessment
Identify nodes that show rearing-mediated volumetric and FA changes to investigate structural connectivity alterations between nodes, as well as modifications in the brain global and regional network properties (also see Note 2). These nodes will be identical for both left and right hemispheres.

Assessment of brain structural connectivity using fiber tractography
Upon pre-processing the data and selection of potential brain nodes, execute the following steps accordingly for each individual subject to map axonal projections between nodes using probabilistic fiber tractography in MRtrix: Step 1: From the pre-processed raw data, estimate the response function for spherical deconvolution (command: dwi2response) (Tournier et al., 2012, Tournier et al., 2013. Specify the algorithm name 'tournier' (other options: dhollander, manual, fa, msmt_5tt, tax), gradient table, brain mask, and the maximum harmonic degree (lmax = 6).
Step 2: Estimate the whole brain fiber orientation distribution (FOD) map from the pre-processed raw data and respective response function (command: dwi2fod) (Tournier et al., 2007). Define the algorithm name 'CSD,' gradient table, and brain mask.
Step 3. Generate the whole brain fiber tractogram from the FOD map (command: tckgen) (Tournier et al., 2009). Use the whole brain mask as the 'seed region' to enable tracking fibers throughout the brain for whole brain tractography (whole brain tractogram) ( Figure 4A). Set the tractography method to probabilistic, the FOD amplitude cut-off to 0.05, the minimum length of the fiber to 3 mm, and the target number of the streamlines to be counted to 5 million.
Step 4: For node-to-node tractography, the whole brain tractography in step 3 may not generate 11 www.bio-protocol.org/e4221 enough streamlines for small nodes (e.g., amygdala). Further increasing the total number of streamlines (> 5 million) may not resolve this issue but requires significant computational resources.
In this case, extract the regions of interest (ROIs) from the atlas co-registered into the subject's native space using Matlab. Next, define a specific node as 'seed region' to initiate the fiber tracking from and another node as 'target' to define the fiber termination point. Then use these two nodes to extract the streamlines connecting two nodes (seed and target) using the tckedit command ( Figure   4B). Consider two nodes as 'connected' if there is at least one streamline terminating at the target node; otherwise, they are 'not connected.' A. Estimation of mouse whole brain fiber tractogram from the fiber orientation distribution (FOD) map. Red, green, and blue colors represent the fiber projections in x, y, and z-axis, respectively.
Five million fibers were generated from each subject; 100 K streamlines were extracted for better visualization of the brain structures. B. Extraction of fibers connecting two specific nodes (seed = amygdala and target = PFC).

Generating brain structural connectome matrix
Repeat step 4 to estimate the structural connections between all possible pairs of nodes (ignore intra-regional connectivity) for both hemispheres ( Figure 5A  A. Extraction of fibers connecting seed and target nodes. B. Generation of structural connectome from the tractograms estimated from selected seed and target nodes. Blue cells correspond to the tractograms shown in A, and white cells indicate intra-regional connectivity (not counted). C. Use the GRETNA software to compute global and regional brain network properties. Panels on the left list all possible properties available for computation. Select the properties based on the study design and transfer them to the pipeline option on the right panel using the respective arrows. Load the connectome matrix for all subjects belonging to one group with specific group ID and then load for the next group with different ID. Specify the output folder to store the results and define the network configuration. Finally, hit the 'Run' button to start computation.

Brain network properties analysis
Use the Matlab based Graph theoretical network analysis toolbox (GRETNA) to compute the brain global and regional network properties (Wang et al., 2015). Perform the following steps accordingly for brain network-based analysis ( Figure 5C): 1. Create an individual data folder containing two sub-folders for left and right hemispheres for each group.
2. Save the connectome matrices as '.mat' files in the respective folders. 13 www.bio-protocol.org/e4221 8. Configure the brain network in the 'Network Analysis' tab as follows:

Parameters Value
Sign of matrix Absolute

Statistical analysis of the estimated structural connectivity and brain network properties
To investigate the effect of rearing and sex on brain structural connectivity and brain network properties, perform a two-way ANOVA with rearing condition (CTL or UPS) and sex as fixed factors, followed by post-hoc comparisons using Tukey's HSD or Sidak's test using GraphPad Prism.

Notes
1. It is very important to check whether structural labels were correctly transferred and show good agreement with the corresponding structures. We recommend refining the segmentation manually, slice by slice, along the axial orientation, forfeiting attention to the other two orientations as well as to the slices preceding and following if necessary.
2. Selection of nodes for brain network analysis is crucial. Using unbiased voxel-based analyses, identify only those nodes which show UPS-mediated volumetric and FA alterations and are highly connected based on the Allen Mouse Brain Connectivity Atlas (Oh et al., 2014).
Furthermore, selected nodes should be non-overlapping, having a unique set of connections to